In the field of mental health research, accurately detecting depression is crucial. However, when handling multimodal long-temporal data, two major challenges emerge: 1) Redundancy exists in long-temporal data feature extraction, and key feature extraction is unclear. 2) Existing multimodal data feature fusion methods are disrupted by inferior modality.

A University of Sydney quantum physicist has developed a new approach to quantum error correction - using lattice gauge theory - that could significantly reduce the number of physical qubits required to build large-scale, fault-tolerant quantum computers.

Observing the Taurus Molecular Cloud, a research team led by Kyushu University has found that during the early growth period of a baby star, the protostellar disk blows magnetic flux 1,000 au in size and creates a giant, relatively warm ring. Describing these phenomena as a baby star’s “sneezes,” these expulsions of energy and gas help the star to properly develop.

Automated lesion segmentation is essential for DR screening, but current deep learning models often lack robustness, generating false positives in low-contrast or artifact-heavy regions. This instability largely stems from a lack of anatomical understanding. While incorporating vessel structures can guide the model, obtaining pixel-level vessel annotations for training is notoriously expensive and scarce.

To address this dilemma, the research team proposed MedFuse on 15 March 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

Sodium-ion batteries are promising alternatives to lithium-ion batteries for large-scale energy storage, enabling lower-cost and safer energy storage systems. O3-type layered oxides are considered mainstream cathodes materials for practical sodium-ion batteries owing to their high theoretical capacity and scalable production, drawing wide attention from both academia and industry. Nevertheless, their limited capacity within 2.0–4.0 V restricts market competitiveness.

Raising the voltage causes lattice oxygen instability, irreversible phase transitions, and electrolyte decomposition, resulting in structural degradation and rapid performance fading, which blocks their commercial application.

To address these issues, a research team led by Prof. ZHANG Xian-Ming from Taiyuan University of Technology has recently proposed an integrated design concept based on solid-solution reactions and anionic redox chemistry. They successfully developed a low-cost, high-capacity, long-life, and air-stable 4.3 V-class O3-type layered oxide cathode material, NaNi_0.35Fe_0.2Mg_0.05Mn_0.3Ti_0.1O₂ (FMT), fundamentally addressing the two critical problems of irreversible P3→O1 phase transition and lattice oxygen release at high voltages.

The team’s findings were published in Science Bulletin .

Sub-headline: HIT (Shenzhen) researchers develop FedPD to enhance personalized cross-architecture collaboration

Researchers from Harbin Institute of Technology (Shenzhen) proposed FedPD, a personalized federated learning method based on partial distillation. By assessing knowledge relevance for selective transfer, FedPD enables efficient collaboration among clients with diverse model architectures while significantly improving performance on heterogeneous data.

Privacy-preserving feature selection allows identifying more important features while ensuring data privacy, thus enhancing data quality. Secure multiparty computation (MPC) is a cryptographic method that allows effective data processing without a trusted third party. However, most MPC-based feature selection schemes overlook the correlation between features and perform poorly for model training when handling datasets containing both numerical and categorical attributes.